Bayesian Nonparametric State-Space Model for System Identification with Distinguishable Multimodal Dynamics

2021 ◽  
Vol 18 (3) ◽  
pp. 116-131
Author(s):  
Young-Jin Park ◽  
Soon-Seo Park ◽  
Han-Lim Choi
Keyword(s):  
System Identification ◽  
State Space ◽  
State Space Model ◽  
Bayesian Nonparametric ◽  
Space Model

State-space Model Identification Using Input and Output Data With Steady State Values Zeroing Multiple Integrals of Output Error

2005 ◽  
Vol 128 (3) ◽  
pp. 746-749
Author(s):  
Manabu Kosaka ◽  
Hiroshi Uda ◽  
Eiichi Bamba ◽  
Hiroshi Shibata
Keyword(s):  
Steady State ◽  
System Identification ◽  
State Space ◽  
State Space Model ◽  
Hankel Matrix ◽  
Output Data ◽  
Single Input Single Output ◽  
Space Model ◽  
Output Error ◽  
Input And Output

This study proposes a new deterministic off-line identification method that obtains a state-space model using input and output data with steady state values. This method comprises of two methods: Zeroing the 0∼N-tuple integral values of the output error of single-input single-output transfer function model (Kosaka et al., 2004) and Ho-Kalman’s method (Zeiger and McEwen, 1974). Herein, we present a new method to derive a matrix similar to the Hankel matrix using multi-input and multi-output data with steady state values. State space matrices A, B, C, and D are derived from the matrix by the method shown in Zeiger and McEwen, 1974 and Longman and Juang, 1989. This method’s utility is that the derived state-space model is emphasized in the low frequency range under certain conditions. Its salient feature is that this method can identify use of step responses; consequently, it is suitable for linear mechanical system identification in which noise and vibration are unacceptable. Numerical simulations of multi-input multi-output system identification are illustrated.

scholarly journals STATE-SPACE MODEL AND IMPLEMENTATION POLISH POWER EXCHANGE IN MATLAB AND SIMULNK ENVIRONMENTS

2017 ◽  
Vol 6 (4) ◽  
pp. 294-308
Author(s):  
Radosław Marlęga
Keyword(s):  
System Identification ◽  
State Space ◽  
Equations Of State ◽  
State Space Model ◽  
Continuous Model ◽  
Parametric Model ◽  
State Variables ◽  
Space Model ◽  
Power Exchange ◽  
The Matrix

This work contains selected results of research on modelling identification of Polish Power Exchange (TGEE) on the example of the figures quoted on the Day Ahead Market (DAM) on TGEE in Poland. In order to obtain a model of the TGEE system on the beginning it was conducted to identify the figures for the period 01.01.2013-31.12.2015 obtaining discrete parametric model arx in MATLAB and Simulink environments using System Identification Toolbox (SIT). The resultant model was converted to a continuous parametric model, and that one on a continuous model in the state space. On the basis of obtained equations of state and outputs, there was interpreted a state variables and parameters of the selected model, i.e. selected elements of the matrix A and matrix B. Research continues.

Sign in / Sign up

Export Citation Format

Share Document